Unmanned cluster construction and quality control method for asphalt pavement

Abstract

The invention discloses an asphalt pavement unmanned cluster construction and quality control method. The method comprises the following steps: S1, constructing and initially debugging an unmanned cluster system; s2, environment perception and data acquisition; s3, optimizing a dynamic path and adjusting a construction strategy; s4, performing real-time quality detection and feedback adjustment; and S5, data analysis and system optimization. By monitoring the environment change, the construction progress and the equipment operation state in real time and automatically adjusting the construction path and the equipment operation mode, the smoothness and high efficiency of the construction process are ensured, and the equipment idle time and the manual scheduling cost are reduced, so that the overall construction efficiency is remarkably improved; key quality parameters in the asphalt laying process can be monitored in real time, quality deviation is automatically detected in the construction process, and the operation mode or path of construction equipment is adjusted in real time.

Description

Technical Field

[0001] The present invention relates to the technical field of asphalt pavement construction, and in particular to a method for unmanned cluster construction and quality control of asphalt pavement. Background Art

[0002] In recent years, with the continuous development of transportation infrastructure, especially in the construction of highways and urban roads in the process of urbanization, the laying and quality control of asphalt pavements have become an important issue in the construction field. Traditional asphalt pavement construction usually relies on manual operation and mechanical equipment for construction, and the construction process is sensitive to environmental conditions such as temperature, humidity, construction speed, etc., and requires manual intervention and precise control to ensure the quality of the pavement and construction progress. With the rapid development of automation and intelligent technology, unmanned driving technology and artificial intelligence have been introduced into the field of asphalt pavement construction, which has promoted the improvement of construction efficiency and quality. Unmanned cluster construction technology, combined with environmental perception technology and real-time feedback control, has gradually become a cutting-edge construction method.

[0003] Existing asphalt pavement construction technology mainly relies on the collaborative work of manual operation and mechanical equipment. During the construction process, it often faces problems such as uneven construction progress, fluctuating pavement quality, and unreasonable equipment scheduling. Although some automated systems have been applied to asphalt pavement construction, most of them lack the ability to dynamically adjust paths in response to real-time environmental changes. In traditional path planning, construction equipment usually relies on predetermined routes and fixed-point control, which makes it difficult to cope with changes brought about by real-time environmental perception data. In addition, there is no effective optimization mechanism for the collaboration and scheduling between different equipment during the construction process, resulting in low construction efficiency and equipment utilization. Therefore, it is necessary to design a method for unmanned cluster construction and quality control of asphalt pavements to solve the above problems. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the problems existing in the above-mentioned existing method for unmanned cluster construction and quality control of asphalt pavement, the present invention is proposed.

[0006] Therefore, the purpose of the present invention is to provide a method for unmanned cluster construction and quality control of asphalt pavement, which is suitable for solving the problem that it is difficult to cope with the changes brought about by real-time environmental perception data, and there is no effective optimization mechanism for the collaboration and scheduling between different equipment during the construction process, resulting in low construction efficiency and equipment utilization.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for unmanned cluster construction and quality control of asphalt pavement, comprising the following steps:

[0008] S1: Unmanned swarm system construction and initial debugging;

[0009] S2: Environmental perception and data collection;

[0010] S3: Dynamic path optimization and construction strategy adjustment;

[0011] S4: Real-time quality detection and feedback adjustment;

[0012] S5: Data analysis and system optimization.

[0013] As a preferred solution of the method for unmanned cluster construction and quality control of asphalt pavement described in the present invention, the dynamic path optimization and construction strategy adjustment are achieved by the following steps:

[0014] S31: construct a path distance calculation formula based on the real-time environment perception data to obtain the path distance between the unmanned construction equipment and the construction point;

[0015] S32: dynamically adjust the construction equipment path based on the real-time construction progress;

[0016] S33: adjusting the equipment operation mode in real time according to the operation status of the construction equipment;

[0017] The path distance calculation formula is as follows:

[0018]

[0019] Among them, D i (t) represents the total path distance between the i-th equipment and all construction points j at time t, p i (t) represents the position coordinates of the i-th device at time t, q j (t) represents the position coordinates of the jth construction point at time t, and M represents the number of construction points.

[0020] As a preferred solution of the method for unmanned cluster construction and quality control of asphalt pavement described in the present invention, the path distance threshold is set as θ according to the output result of the path distance calculation formula. D ,This threshold represents the path distance set according to the size of the ,construction site, the number of construction points and the ,performance of the equipment;

[0021] If D i (t)>θ D , indicating that the current path distance is too long, triggering path optimization adjustment;

[0022] If Di (t)≤θ D , indicating that the current path distance is appropriate and there is no need to optimize the path.

[0023] As a preferred solution of the method for unmanned cluster construction and quality control of asphalt pavement described in the present invention, the environmental impact factor calculation formula is constructed according to the output result of the path distance calculation formula, and the environmental impact factor calculation formula is as follows:

[0024]

[0025] Among them, E i (t) represents the environmental impact factor of the i-th device at time t, C i (t) represents the environmental change parameter of the location of the i-th device, ∈ is a small constant to avoid division by zero error, and α is a constant to adjust the degree of environmental impact.

[0026] As a preferred solution of the method for unmanned cluster construction and quality control of asphalt pavement described in the present invention, the environmental impact threshold value θ is constructed according to the output result of the environmental impact factor calculation formula. E ;

[0027] If E i (t)>θ E , indicating that environmental factors have an impact on path planning and the path needs to be adjusted;

[0028] If E i (t)≤θ E , indicating that environmental factors have no effect on path planning or the effect is so small that it can be ignored, and the unmanned equipment continues to construct along the current path.

[0029] As a preferred solution of the method for unmanned cluster construction and quality control of asphalt pavement described in the present invention, a comprehensive calculation formula for construction progress and path optimization is constructed according to the output result of the environmental impact factor calculation formula:

[0030]

[0031] Among them, S i (t) represents the path optimization score of the i-th equipment at time t, T(t) represents the construction progress, and β and γ represent the weight coefficients of construction progress and path optimization, respectively.

[0032] As a preferred solution of the method for unmanned cluster construction and quality control of asphalt pavement described in the present invention, two thresholds are set according to the output result of the comprehensive calculation formula of construction progress and path optimization, and the two thresholds are θ S1 and θ S2 , where θS1 Used to judge the basic feasibility of the path, θ S2 Used to judge the efficiency of path optimization and ensure that path planning meets construction efficiency and quality requirements;

[0033] If S i (t)<θ S1 , indicating that the path planning is unreasonable and the path needs to be readjusted;

[0034] If θ S1 ≤S i (t)≤θ S2 , indicating that the path has been optimized, but has not reached the expected construction efficiency or quality standards, and the path needs to be further optimized;

[0035] If S i (t)≥θ S2 , indicating that the path planning score is high and the path optimization effect is good, meeting the construction efficiency and quality requirements.

[0036] As a preferred solution of the method for unmanned cluster construction and quality control of asphalt pavement described in the present invention, the environmental change parameters include pavement flatness, humidity and temperature.

[0037] As a preferred solution of the method for unmanned cluster construction and quality control of asphalt pavement described in the present invention, the real-time quality detection and feedback adjustment are achieved by the following steps:

[0038] S41: Monitoring quality parameters during asphalt paving using integrated sensors;

[0039] S42: Use a high-definition camera to detect the surface of the asphalt road surface;

[0040] S43: Compare the temperature, humidity and compaction data of the paving area with the quality standards to automatically identify whether quality deviations occur during the construction process;

[0041] S44: When a quality problem is detected, the system issues a warning and immediately initiates an adjustment mechanism to adjust the operation mode or construction path of the construction equipment.

[0042] As a preferred solution of the method for unmanned cluster construction and quality control of asphalt pavement described in the present invention, the unmanned cluster system includes unmanned construction equipment and a construction environment perception module, and the construction environment perception module is used to collect environmental data of the construction area in real time.

[0043] Beneficial effects of the present invention: Through the unmanned cluster collaboration system and dynamic path optimization, the present invention can greatly improve the construction efficiency. The present invention monitors environmental changes, construction progress and equipment operating status in real time, automatically adjusts the construction path and equipment operation mode, ensures a smooth and efficient construction process, reduces equipment idle time and manual scheduling costs, and thus significantly improves the overall construction efficiency. It can monitor key quality parameters in the asphalt paving process in real time, automatically detect quality deviations during construction, and instantly adjust the operation mode or path of construction equipment, avoiding the accumulation and expansion of quality problems. During the construction process, the system can automatically adjust the construction strategy to adapt to different climates, road conditions and construction scales. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0045] Figure 1 A schematic diagram of the steps of a method for unmanned cluster construction and quality control of asphalt pavement proposed by the present invention;

[0046] Figure 2 A schematic diagram of the framework structure of an unmanned cluster system for an unmanned cluster construction and quality control method of an asphalt pavement proposed by the present invention;

[0047] Figure 3 This is a cloud diagram of the unmanned cluster compaction structure of an asphalt pavement according to the method for unmanned cluster construction and quality control of an asphalt pavement proposed in the present invention. DETAILED DESCRIPTION

[0048] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0049] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0050] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0051] Secondly, the present invention is described in detail with reference to the schematic diagram. When describing the embodiments of the present invention in detail, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0052] Embodiment 1

[0053] Reference Figure 1-Figure 3 , as an embodiment of the present invention, provides a method for unmanned cluster construction and quality control of asphalt pavement, comprising the following steps:

[0054] S1: Unmanned swarm system construction and initial debugging;

[0055] The unmanned cluster system includes unmanned construction equipment and a construction environment perception module. The construction environment perception module is used to collect environmental data (temperature, humidity, air quality, road surface flatness, etc.) of the construction area in real time. The unmanned construction equipment includes unmanned pavers, rollers, self-driving transport vehicles and other equipment. The environment perception module includes laser radars, infrared sensors, high-definition cameras, temperature and humidity sensors and other devices.

[0056] The combination of unmanned construction equipment and environmental perception modules has achieved a high degree of automation in the construction process, improved the accuracy and efficiency of construction, reduced manual intervention, and lowered construction risks.

[0057] S2: Environmental perception and data collection;

[0058] Specifically, the laser radar is used to scan the road surface to obtain the geometric shape, potholes and other irregular surface information of the asphalt road surface in real time. The infrared sensor monitors the laying temperature of the asphalt. The high-definition camera collects high-precision road surface images to monitor key quality parameters such as laying thickness, density, and uniformity. The temperature and humidity sensor senses the ambient temperature and humidity of the construction site.

[0059] S3: Dynamic path optimization and construction strategy adjustment;

[0060] S31: construct a path distance calculation formula based on the real-time environment perception data to obtain the path distance between the unmanned construction equipment and the construction point;

[0061] S32: dynamically adjust the construction equipment path based on the real-time construction progress;

[0062] S33: adjusting the equipment operation mode in real time according to the operation status of the construction equipment;

[0063] By combining the feedback of real-time environmental data with the construction progress, the construction path and equipment operation mode are dynamically adjusted, effectively avoiding equipment idleness or inefficient operation caused by improper path selection, and improving construction efficiency;

[0064] S4: Real-time quality detection and feedback adjustment;

[0065] S41: Monitoring quality parameters during asphalt paving using integrated sensors;

[0066] S42: Use a high-definition camera to detect the surface of the asphalt road surface;

[0067] S43: Compare the temperature, humidity and compaction data of the paving area with the quality standards to automatically identify whether quality deviations occur during the construction process;

[0068] S44: When a quality problem is detected, the system issues a warning and immediately initiates an adjustment mechanism to adjust the operation mode or construction path of the construction equipment;

[0069] The present invention monitors the quality parameters of asphalt paving in real time by integrating sensors and high-definition cameras, and automatically adjusts the construction process according to real-time data, which not only improves the stability of construction quality but also ensures the accuracy during the construction process;

[0070] S5: Data analysis and system optimization;

[0071] Collect all data feedback from various sensors during the construction process, including environmental data, equipment operation data and construction quality data, conduct data analysis, optimize the path planning and quality control plan for the next construction based on historical construction data and real-time monitoring data, and use big data analysis and artificial intelligence technology to optimize the system's prediction capabilities, so that the system can automatically adjust construction strategies under different climates, road conditions, construction scales, etc.

[0072] The path distance calculation formula is as follows:

[0073]

[0074] Among them, D i (t) represents the total path distance between the i-th equipment and all construction points j at time t, p i (t) represents the position coordinates of the i-th device at time t, q j (t) represents the position coordinates of the jth construction point at time t, and M represents the number of construction points.

[0075] According to the output result of the path distance calculation formula, the path distance threshold is set to θ D ,This threshold represents the path distance set according to the size of the ,construction site, the number of construction points and the ,performance of the equipment;

[0076] If D i (t)>θ D , indicating that the current path distance is too long, triggering path optimization adjustment;

[0077] If D i (t)≤θ D , indicating that the current path distance is appropriate and there is no need to optimize the path.

[0078] The environmental impact factor calculation formula is constructed according to the output result of the path distance calculation formula, and the environmental impact factor calculation formula is as follows:

[0079]

[0080] Among them, E i (t) represents the environmental impact factor of the i-th device at time t, C i (t) represents the environmental variation parameters at the location of the i-th device. The environmental variation parameters include road surface flatness, humidity and temperature. ∈ is a small constant to avoid division by zero error. α is a constant to adjust the degree of environmental impact.

[0081] According to the output result of the environmental impact factor calculation formula, the environmental impact threshold is constructed as θ E ;

[0082] If E i (t)>θ E , indicating that environmental factors have an impact on path planning and the path needs to be adjusted;

[0083] If E i (t)≤θ E , indicating that environmental factors have no effect on path planning or the effect is so small that it can be ignored, and the unmanned equipment continues to construct along the current path.

[0084] According to the output results of the environmental impact factor calculation formula, a comprehensive calculation formula for construction progress and path optimization is constructed:

[0085]

[0086] Among them, S i (t) represents the path optimization score of the i-th equipment at time t, T(t) represents the construction progress, and β and γ represent the weight coefficients of construction progress and path optimization, respectively.

[0087] According to the output results of the comprehensive calculation formula of construction progress and path optimization, two thresholds are set, and the two thresholds are θ S1 and θ S2 , where θ S1 Used to judge the basic feasibility of the path, θS2 Used to judge the efficiency of path optimization and ensure that path planning meets construction efficiency and quality requirements;

[0088] If S i (t)<θ S1 , indicating that the path planning is unreasonable and the path needs to be readjusted;

[0089] If θ S1 ≤S i (t)≤θ S2 , indicating that the path has been optimized, but has not reached the expected construction efficiency or quality standards, and the path needs to be further optimized;

[0090] If S i (t)≥θ S2 , indicating that the path planning score is high and the path optimization effect is good, meeting the construction efficiency and quality requirements.

[0091] During use, the present invention can greatly improve construction efficiency through the unmanned cluster collaboration system and dynamic path optimization. The present invention monitors environmental changes, construction progress and equipment operating status in real time, automatically adjusts the construction path and equipment operating mode, ensures a smooth and efficient construction process, reduces equipment idle time and manual scheduling costs, and thus significantly improves the overall construction efficiency. It can monitor key quality parameters in the asphalt paving process in real time, automatically detect quality deviations during construction, and adjust the operating mode or path of construction equipment in real time, avoiding the accumulation and expansion of quality problems. During the construction process, the system can automatically adjust the construction strategy to adapt to different climates, road conditions and construction scales.

[0092] Embodiment 2

[0093] Referring to Tables 1 to 3, this embodiment is different from the first embodiment in that, in order to verify its beneficial effects, experimental comparative data between the present invention and the prior art are provided. Specifically, in order to verify the effects of the present invention, two test sections with a length of 500 meters are selected, wherein Section A adopts the traditional construction method, and Section B adopts the unmanned cluster construction method of the present invention. The construction environment settings are the same, and both are carried out under the conditions of clear weather, temperature of 25°C, and humidity of 60%.

[0094] Section A uses traditional manual construction equipment, including pavers, rollers, and manual monitoring equipment; Section B uses unmanned construction equipment and an environmental perception module, which records environmental data. The data is uploaded to the central control system in real time for dynamic path planning and quality monitoring.

[0095] During the construction process, Section B dynamically planned the equipment path by constructing a path distance calculation formula and adjusted the operation mode based on the construction progress and equipment status. Section A completely relied on manual experience to adjust the equipment path and mode.

[0096] Section B uses integrated sensors and high-definition cameras to monitor key quality parameters such as paving temperature, humidity, and compactness in real time, automatically identifying deviations and issuing adjustment instructions. Section A manually conducts quality checks, and if problems are found, construction equipment needs to be manually adjusted;

[0097] After the construction was completed, the environmental data, equipment operation data and construction quality data of the two sections of road construction were collected for comparative analysis. The path planning algorithm of section B was further optimized based on historical data, and the data obtained from the experiment were sorted into the following three tables.

[0098] Table 1: Construction efficiency comparison table

[0099]

[0100] Table 2: Construction quality comparison table

[0101]

[0102]

[0103] Table 3: Comparison of environmental data utilization

[0104]

[0105] Through the analysis of the data in Tables 1 to 3 above, the construction efficiency of Section B is significantly higher than that of Section A. The average operation time of its equipment is reduced by 33.3%, the downtime is reduced by 66.7%, and the dynamic path optimization reduces the number of path adjustments by 4 times (see Table 1). In terms of construction quality, the flatness of Section B is improved by 48.6%, the compaction is improved by 5.4%, and the temperature deviation is reduced to ±1.5℃, ensuring the uniformity and consistency of construction quality (see Table 2);

[0106] The invention effectively utilizes environmental data during the construction process. The number of sensing data collection points is five times that of traditional methods, and the data utilization rate reaches 95%. Through real-time feedback to adjust the construction path and equipment operation mode, the construction efficiency and quality are improved simultaneously (see Table 3);

[0107] In summary: the method based on unmanned cluster construction and quality control of the present invention significantly improves construction efficiency, reduces resource waste, and significantly improves construction quality. Compared with the existing technology, the present invention shows strong creativity in data utilization and construction path optimization.

[0108] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for unmanned cluster construction and quality control of asphalt pavement, characterized in that: The following steps are involved: S1: Unmanned swarm system construction and initial debugging; S2: Environmental perception and data collection; S3: Dynamic path optimization and construction strategy adjustment; S4: Real-time quality detection and feedback adjustment; S5: Data analysis and system optimization.

2. The method for unmanned cluster construction and quality control of asphalt pavement according to claim 1 is characterized by: The dynamic path optimization and construction strategy adjustment are achieved through the following steps: S31: construct a path distance calculation formula based on the real-time environment perception data to obtain the path distance between the unmanned construction equipment and the construction point; S32: dynamically adjust the construction equipment path based on the real-time construction progress; S33: adjusting the equipment operation mode in real time according to the operation status of the construction equipment; The path distance calculation formula is as follows: Among them, D i (t) represents the total path distance between the i-th equipment and all construction points j at time t, p i (t) represents the position coordinates of the i-th device at time t, q j (t) represents the position coordinates of the jth construction point at time t, and M represents the number of construction points.

3. The method for unmanned cluster construction and quality control of asphalt pavement according to claim 2 is characterized by: According to the output result of the path distance calculation formula, the path distance threshold is set to θ D ,This threshold represents the path distance set according to the size of the construction site, the number of construction points, and the equipment performance; If D i (t)>θ D , indicating that the current path distance is too long, triggering path optimization adjustment; If D i (t)≤θ D , indicating that the current path distance is appropriate and there is no need to optimize the path.

4. The method for unmanned cluster construction and quality control of asphalt pavement according to claim 2 is characterized by: The environmental impact factor calculation formula is constructed according to the output result of the path distance calculation formula, and the environmental impact factor calculation formula is as follows: Among them, E i (t) represents the environmental impact factor of the i-th device at time t, C i (t) represents the environmental change parameter of the location of the i-th device, ∈ is a small constant to avoid division by zero error, and α is a constant to adjust the degree of environmental impact.

5. The method for unmanned cluster construction and quality control of asphalt pavement according to claim 4 is characterized by: According to the output result of the environmental impact factor calculation formula, the environmental impact threshold is constructed as θ E ; If E i (t)>θ E , indicating that environmental factors have an impact on path planning and the path needs to be adjusted; If E i (t)≤θ E , indicating that environmental factors have no effect on path planning or the effect is so small that it can be ignored, and the unmanned equipment continues to construct along the current path.

6. The method for unmanned cluster construction and quality control of asphalt pavement according to claim 4 is characterized by: According to the output results of the environmental impact factor calculation formula, a comprehensive calculation formula for construction progress and path optimization is constructed: Among them, S i (t) represents the path optimization score of the i-th equipment at time t, T(t) represents the construction progress, and β and γ represent the weight coefficients of construction progress and path optimization, respectively.

7. The method for unmanned cluster construction and quality control of asphalt pavement according to claim 6 is characterized by: According to the output results of the comprehensive calculation formula of construction progress and path optimization, two thresholds are set, and the two thresholds are θ S1 and θ S2 , where θ S1 Used to judge the basic feasibility of the path, θ S2 Used to judge the efficiency of path optimization and ensure that path planning meets construction efficiency and quality requirements; If S i (t)<θ S1 , indicating that the path planning is unreasonable and the path needs to be readjusted; If θ S1 ≤S i (t)≤θ S2 , indicating that the path has been optimized, but has not reached the expected construction efficiency or quality standards, and the path needs to be further optimized; If S i (t)≥θ S2 , indicating that the path planning score is high and the path optimization effect is good, meeting the construction efficiency and quality requirements.

8. The method for unmanned cluster construction and quality control of asphalt pavement according to claim 4 is characterized by: The environmental change parameters include road surface flatness, humidity and temperature.

9. The method for unmanned cluster construction and quality control of asphalt pavement according to claim 1, characterized in that: The real-time quality detection and feedback adjustment are achieved by the following steps: S41: Monitoring quality parameters during asphalt paving using integrated sensors; S42: Use a high-definition camera to detect the surface of the asphalt road surface; S43: Compare the temperature, humidity and compaction data of the paving area with the quality standards to automatically identify whether quality deviations occur during the construction process; S44: When a quality problem is detected, the system issues a warning and immediately initiates an adjustment mechanism to adjust the operation mode or construction path of the construction equipment.

10. The method for unmanned cluster construction and quality control of asphalt pavement according to claim 1, characterized in that: The unmanned cluster system includes unmanned construction equipment and a construction environment perception module, and the construction environment perception module is used to collect environmental data of the construction area in real time.

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